Data Science Manager – Fraud Risk Engine
Content + Source + Freshness • 17 Dec 2025 • 95% confidence
Offer value
Strong role in a critical area of fraud detection with high demand for expertise in data science and analytics, particularly in financial services.
- Focus on a critical fraud risk detection role
- Involvement with advanced AI and data technologies
- Strong growth potential in the analytics field
- Requires advanced skills and sector-specific experience
Pros
- Role essential in combating financial crime
- Work with advanced technologies and analytical tools
- High demand for skills in fraud analytics and compliance
Cons
- Requires niche skills that may be less transferable
- Potential pressure due to the critical nature of fraud prevention
- Competing against candidates with similar high-level expertise
Who it's for
Mid / Senior • Hybrid based in Melbourne
Good fit
- Data science professionals with a focus on fraud prevention
- Individuals experienced in using machine learning for analytics
- Candidates with strong financial services domain knowledge
Not recommended for
- Entry-level data analysts or scientists
- Individuals without a background in financial crime
- Candidates lacking technical proficiency in relevant tools
Motivation fit
Key skills
About the job
We are seeking a highly skilled and experienced Fraud Risk Engine Decision Scientist to lead the design, implementation, and optimisation of real-time fraud detection use cases using a fraud risk engine. This role bridges business understanding with technical expertise, enabling proactive fraud prevention through data-driven decisioning.
You will collaborate closely with business stakeholders to define fraud scenarios, identify and source relevant data, configure detection logic (rules, thresholds, models), and ensure accurate and timely decisions and alerts.
The ideal candidate will have extensive experience in fraud risk engine product implementation, fraud detection techniques, and a strong understanding of financial crime prevention strategies, including the use of cloud-based data services like AWS.
Key Responsibilities:
Partner with business teams to define and refine fraud detection use cases based on emerging threats, customer behaviour, and regulatory needs.
Identify, source, clean, and organise relevant data to support fraud detection. Perform exploratory data analysis to uncover patterns and insights.
Configure and tune rules, thresholds, and predictive models within the fraud risk engine to enable real-time decisioning.
Translate business needs into statistical or machine learning models. Validate and monitor model performance, ensuring alignment with governance standards.
Ensure fraud decisions are accurate, timely, and actionable. Design alerting mechanisms that balance fraud prevention with customer experience.
Present analytical findings and model outcomes to technical and non-technical stakeholders. Provide recommendations for continuous improvement.
Collaborate with cross-functional teams including architects, engineering, compliance, and business units to implement new fraud prevention initiatives.
Provide expertise during audits and compliance reviews related to fraud prevention controls.
